Importance Children with medical complexity (CMC) have substantial health care needs and frequently experience poor health care quality. Understanding the population prevalence and associated health care needs can inform clinical and public health initiatives. Objective To estimate the prevalence of CMC using open-source pediatric algorithms, evaluate performance of these algorithms in predicting health care utilization and in-hospital mortality, and identify associations between medical complexity as defined by these algorithms and clinical outcomes. Design, Setting, and Participants This retrospective cohort study used all-payer claims data from Colorado, Massachusetts, and New Hampshire from 2012 through 2017. Children and adolescents younger than 18 years residing in these states were included if they had 12 months or longer of enrollment in a participating health care plan. Analyses were conducted from March 12, 2021, to January 7, 2022. Exposures The pediatric Complex Chronic Condition Classification System, Pediatric Medical Complexity Algorithm, and Children With Disabilities Algorithm were applied to 3 years of data to identify children with complex and disabling conditions, first in their original form and then using more conservative criteria that required multiple health care claims or involvement of 3 or more body systems. Main Outcomes and Measures Primary outcomes, examined over 2 years, included in-hospital mortality and a composite measure of health care services, including specialized therapies, specialized medical equipment, and inpatient care. Outcomes were modeled using logistic regression. Model performance was evaluated using C statistics, sensitivity, and specificity. Results Of 1 936 957 children, 48.4% were female, 87.8% resided in urban core areas, and 45.1% had government-sponsored insurance as their only primary payer. Depending on the algorithm and coding criteria applied, 0.67% to 11.44% were identified as CMC. All 3 algorithms had adequate discriminative ability, sensitivity, and specificity to predict in-hospital mortality and composite health care services (C statistic = 0.76 [95% CI, 0.73-0.80] to 0.81 [95% CI, 0.78-0.84] for mortality and 0.77 [95% CI, 0.76-0.77] to 0.80 [95% CI, 0.79-0.80] for composite health care services). Across algorithms, CMC had significantly greater odds of mortality (adjusted odds ratio [aOR], 9.97; 95% CI, 7.70-12.89; to aOR, 69.35; 95% CI, 52.52-91.57) and composite health care services (aOR, 4.59; 95% CI, 4.44-4.73; to aOR, 18.87; 95% CI, 17.87-19.93) than children not identified as CMC. Conclusions and Relevance In this study, open-source algorithms identified different cohorts of CMC in terms of prevalence and magnitude of risk, but all predicted increased health care utilization and in-hospital mortality. These results can inform research, programs, and policies for CMC.

中文翻译：

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儿童医疗复杂性的患病率以及与医疗保健利用和院内死亡率的关系。

重要性 患有医疗复杂性 (CMC) 的儿童有大量的医疗保健需求，并且经常经历较差的医疗保健质量。了解人口患病率和相关的医疗保健需求可以为临床和公共卫生举措提供信息。目的 使用开源儿科算法估计 CMC 的患病率，评估这些算法在预测医疗保健利用率和院内死亡率方面的性能，并确定这些算法定义的医疗复杂性与临床结果之间的关联。设计、设置和参与者 这项回顾性队列研究使用了 2012 年至 2017 年科罗拉多州、马萨诸塞州和新罕布什尔州的所有付款人索赔数据。居住在这些州的 18 岁以下儿童和青少年，如果他们有 12 个月或更长的时间，则被纳入研究范围。参加参与性医疗保健计划。分析于2021年3月12日至2022年1月7日进行。暴露儿科复杂慢性病分类系统、儿科医疗复杂性算法和残疾儿童算法应用于3年的数据，以识别患有复杂和残疾疾病的儿童，首先以其原始形式，然后使用更保守的标准，要求多种医疗保健声明或涉及 3 个或更多身体系统。主要成果和措施 经过两年多的审查，主要成果包括住院死亡率和医疗保健服务的综合衡量标准，包括专门治疗、专门医疗设备和住院护理。使用逻辑回归对结果进行建模。使用 C 统计量、敏感性和特异性来评估模型性能。结果 1 936 957 名儿童中，48.4% 为女性，87.8% 居住在城市核心地区，45.1% 的儿童以政府资助的保险为唯一主要支付者。根据所应用的算法和编码标准，0.67% 到 11.44% 被识别为 CMC。所有 3 种算法都具有足够的辨别能力、敏感性和特异性来预测院内死亡率和综合医疗保健服务（死亡率 C 统计 = 0.76 [95% CI, 0.73-0.80] 至 0.81 [95% CI, 0.78-0.84]）综合医疗保健服务为 0.77 [95% CI, 0.76-0.77] 至 0.80 [95% CI, 0.79-0.80]）。在各种算法中，CMC 的死亡率（调整后比值比 [aOR]，9.97；95% CI，7.70-12.89；aOR，69.35；95% CI，52.52-91.57）和综合医疗保健服务（aOR，4.59）显着更高。 ; 95% CI, 4.44-4.73; aOR, 18.87; 95% CI, 17.87-19.93) 与未确定为 CMC 的儿童相比。结论和相关性 在这项研究中，开源算法根据患病率和风险程度确定了不同的 CMC 队列，但所有这些都预测医疗保健利用率和院内死亡率会增加。这些结果可以为 CMC 的研究、计划和政策提供信息。